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feat: 3-mode selfhosted refactoring (--gpu, --cpu, --hosted) + audio token auth fallback (#896)

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* fix: local processing instead of http server for cpu

* add fallback token if service worker doesnt work

* chore: rename processors to keep processor pattern up to date and allow other processors to be createed and used with env vars
This commit is contained in:
Juan Diego García
2026-03-04 16:31:08 -05:00
committed by GitHub
parent 4235ab4293
commit a682846645
34 changed files with 2640 additions and 172 deletions
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210
scripts/setup-selfhosted.sh
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@@ -4,11 +4,12 @@
# Single script to configure and launch everything on one server.
#
# Usage:
# ./scripts/setup-selfhosted.sh <--gpu|--cpu> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASSWORD] [--build]
# ./scripts/setup-selfhosted.sh <--gpu|--cpu|--hosted> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASSWORD] [--build]
#
# Specialized models (pick ONE — required):
# --gpu NVIDIA GPU for transcription/diarization/translation
# --cpu CPU-only for transcription/diarization/translation (slower)
# ML processing modes (pick ONE — required):
# --gpu NVIDIA GPU container for transcription/diarization/translation
# --cpu In-process CPU processing (no ML container, slower)
# --hosted Remote GPU service URL (no ML container)
#
# Local LLM (optional — for summarization & topic detection):
# --ollama-gpu Local Ollama with NVIDIA GPU acceleration
@@ -29,6 +30,7 @@
# ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
# ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --domain reflector.example.com
# ./scripts/setup-selfhosted.sh --cpu --ollama-cpu --garage --caddy
# ./scripts/setup-selfhosted.sh --hosted --garage --caddy
# ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --llm-model mistral --garage --caddy
# ./scripts/setup-selfhosted.sh --gpu --garage --caddy --password mysecretpass
# ./scripts/setup-selfhosted.sh --gpu --garage --caddy
@@ -183,11 +185,14 @@ for i in "${!ARGS[@]}"; do
arg="${ARGS[$i]}"
case "$arg" in
--gpu)
[[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu and --cpu. Pick one."; exit 1; }
[[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu, --cpu, and --hosted. Pick one."; exit 1; }
MODEL_MODE="gpu" ;;
--cpu)
[[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu and --cpu. Pick one."; exit 1; }
[[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu, --cpu, and --hosted. Pick one."; exit 1; }
MODEL_MODE="cpu" ;;
--hosted)
[[ -n "$MODEL_MODE" ]] && { err "Cannot combine --gpu, --cpu, and --hosted. Pick one."; exit 1; }
MODEL_MODE="hosted" ;;
--ollama-gpu)
[[ -n "$OLLAMA_MODE" ]] && { err "Cannot combine --ollama-gpu and --ollama-cpu. Pick one."; exit 1; }
OLLAMA_MODE="ollama-gpu" ;;
@@ -224,20 +229,21 @@ for i in "${!ARGS[@]}"; do
SKIP_NEXT=true ;;
*)
err "Unknown argument: $arg"
err "Usage: $0 <--gpu|--cpu> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASS] [--build]"
err "Usage: $0 <--gpu|--cpu|--hosted> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASS] [--build]"
exit 1
;;
esac
done
if [[ -z "$MODEL_MODE" ]]; then
err "No model mode specified. You must choose --gpu or --cpu."
err "No model mode specified. You must choose --gpu, --cpu, or --hosted."
err ""
err "Usage: $0 <--gpu|--cpu> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASS] [--build]"
err "Usage: $0 <--gpu|--cpu|--hosted> [--ollama-gpu|--ollama-cpu] [--llm-model MODEL] [--garage] [--caddy] [--domain DOMAIN] [--password PASS] [--build]"
err ""
err "Specialized models (required):"
err " --gpu NVIDIA GPU for transcription/diarization/translation"
err " --cpu CPU-only (slower but works without GPU)"
err "ML processing modes (required):"
err " --gpu NVIDIA GPU container for transcription/diarization/translation"
err " --cpu In-process CPU processing (no ML container, slower)"
err " --hosted Remote GPU service URL (no ML container)"
err ""
err "Local LLM (optional):"
err " --ollama-gpu Local Ollama with GPU (for summarization/topics)"
@@ -255,7 +261,9 @@ if [[ -z "$MODEL_MODE" ]]; then
fi
# Build profiles list — one profile per feature
COMPOSE_PROFILES=("$MODEL_MODE")
# Only --gpu needs a compose profile; --cpu and --hosted use in-process/remote backends
COMPOSE_PROFILES=()
[[ "$MODEL_MODE" == "gpu" ]] && COMPOSE_PROFILES+=("gpu")
[[ -n "$OLLAMA_MODE" ]] && COMPOSE_PROFILES+=("$OLLAMA_MODE")
[[ "$USE_GARAGE" == "true" ]] && COMPOSE_PROFILES+=("garage")
[[ "$USE_CADDY" == "true" ]] && COMPOSE_PROFILES+=("caddy")
@@ -422,43 +430,102 @@ step_server_env() {
env_set "$SERVER_ENV" "WEBRTC_HOST" "$PRIMARY_IP"
fi
# Specialized models (always via gpu/cpu container aliased as "transcription")
env_set "$SERVER_ENV" "TRANSCRIPT_BACKEND" "modal"
env_set "$SERVER_ENV" "TRANSCRIPT_URL" "http://transcription:8000"
env_set "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY" "selfhosted"
# Specialized models — backend configuration per mode
env_set "$SERVER_ENV" "DIARIZATION_ENABLED" "true"
env_set "$SERVER_ENV" "DIARIZATION_BACKEND" "modal"
env_set "$SERVER_ENV" "DIARIZATION_URL" "http://transcription:8000"
env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "modal"
env_set "$SERVER_ENV" "TRANSLATE_URL" "http://transcription:8000"
env_set "$SERVER_ENV" "PADDING_BACKEND" "modal"
env_set "$SERVER_ENV" "PADDING_URL" "http://transcription:8000"
case "$MODEL_MODE" in
gpu)
# GPU container aliased as "transcription" on docker network
env_set "$SERVER_ENV" "TRANSCRIPT_BACKEND" "modal"
env_set "$SERVER_ENV" "TRANSCRIPT_URL" "http://transcription:8000"
env_set "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY" "selfhosted"
env_set "$SERVER_ENV" "DIARIZATION_BACKEND" "modal"
env_set "$SERVER_ENV" "DIARIZATION_URL" "http://transcription:8000"
env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "modal"
env_set "$SERVER_ENV" "TRANSLATE_URL" "http://transcription:8000"
env_set "$SERVER_ENV" "PADDING_BACKEND" "modal"
env_set "$SERVER_ENV" "PADDING_URL" "http://transcription:8000"
ok "ML backends: GPU container (modal)"
;;
cpu)
# In-process backends — no ML service container needed
env_set "$SERVER_ENV" "TRANSCRIPT_BACKEND" "whisper"
env_set "$SERVER_ENV" "DIARIZATION_BACKEND" "pyannote"
env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "marian"
env_set "$SERVER_ENV" "PADDING_BACKEND" "pyav"
ok "ML backends: in-process CPU (whisper/pyannote/marian/pyav)"
;;
hosted)
# Remote GPU service — user provides URL
local gpu_url=""
if env_has_key "$SERVER_ENV" "TRANSCRIPT_URL"; then
gpu_url=$(env_get "$SERVER_ENV" "TRANSCRIPT_URL")
fi
if [[ -z "$gpu_url" ]] && [[ -t 0 ]]; then
echo ""
info "Enter the URL of your remote GPU service (e.g. https://gpu.example.com)"
read -rp " GPU service URL: " gpu_url
fi
if [[ -z "$gpu_url" ]]; then
err "GPU service URL required for --hosted mode."
err "Set TRANSCRIPT_URL in server/.env or provide it interactively."
exit 1
fi
env_set "$SERVER_ENV" "TRANSCRIPT_BACKEND" "modal"
env_set "$SERVER_ENV" "TRANSCRIPT_URL" "$gpu_url"
env_set "$SERVER_ENV" "DIARIZATION_BACKEND" "modal"
env_set "$SERVER_ENV" "DIARIZATION_URL" "$gpu_url"
env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "modal"
env_set "$SERVER_ENV" "TRANSLATE_URL" "$gpu_url"
env_set "$SERVER_ENV" "PADDING_BACKEND" "modal"
env_set "$SERVER_ENV" "PADDING_URL" "$gpu_url"
# API key for remote service
local gpu_api_key=""
if env_has_key "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY"; then
gpu_api_key=$(env_get "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY")
fi
if [[ -z "$gpu_api_key" ]] && [[ -t 0 ]]; then
read -rp " GPU service API key (or Enter to skip): " gpu_api_key
fi
if [[ -n "$gpu_api_key" ]]; then
env_set "$SERVER_ENV" "TRANSCRIPT_MODAL_API_KEY" "$gpu_api_key"
fi
ok "ML backends: remote hosted ($gpu_url)"
;;
esac
# HuggingFace token for gated models (pyannote diarization)
# Written to root .env so docker compose picks it up for gpu/cpu containers
local root_env="$ROOT_DIR/.env"
local current_hf_token="${HF_TOKEN:-}"
if [[ -f "$root_env" ]] && env_has_key "$root_env" "HF_TOKEN"; then
current_hf_token=$(env_get "$root_env" "HF_TOKEN")
fi
if [[ -z "$current_hf_token" ]]; then
echo ""
warn "HF_TOKEN not set. Diarization will use a public model fallback."
warn "For best results, get a token at https://huggingface.co/settings/tokens"
warn "and accept pyannote licenses at https://huggingface.co/pyannote/speaker-diarization-3.1"
if [[ -t 0 ]]; then
read -rp " HuggingFace token (or press Enter to skip): " current_hf_token
# --gpu: written to root .env (docker compose passes to GPU container)
# --cpu: written to both root .env and server/.env (in-process pyannote needs it)
# --hosted: not needed (remote service handles its own auth)
if [[ "$MODEL_MODE" != "hosted" ]]; then
local root_env="$ROOT_DIR/.env"
local current_hf_token="${HF_TOKEN:-}"
if [[ -f "$root_env" ]] && env_has_key "$root_env" "HF_TOKEN"; then
current_hf_token=$(env_get "$root_env" "HF_TOKEN")
fi
if [[ -z "$current_hf_token" ]]; then
echo ""
warn "HF_TOKEN not set. Diarization will use a public model fallback."
warn "For best results, get a token at https://huggingface.co/settings/tokens"
warn "and accept pyannote licenses at https://huggingface.co/pyannote/speaker-diarization-3.1"
if [[ -t 0 ]]; then
read -rp " HuggingFace token (or press Enter to skip): " current_hf_token
fi
fi
if [[ -n "$current_hf_token" ]]; then
touch "$root_env"
env_set "$root_env" "HF_TOKEN" "$current_hf_token"
export HF_TOKEN="$current_hf_token"
# In CPU mode, server process needs HF_TOKEN directly
if [[ "$MODEL_MODE" == "cpu" ]]; then
env_set "$SERVER_ENV" "HF_TOKEN" "$current_hf_token"
fi
ok "HF_TOKEN configured"
else
touch "$root_env"
env_set "$root_env" "HF_TOKEN" ""
ok "HF_TOKEN skipped (using public model fallback)"
fi
fi
if [[ -n "$current_hf_token" ]]; then
touch "$root_env"
env_set "$root_env" "HF_TOKEN" "$current_hf_token"
export HF_TOKEN="$current_hf_token"
ok "HF_TOKEN configured"
else
touch "$root_env"
env_set "$root_env" "HF_TOKEN" ""
ok "HF_TOKEN skipped (using public model fallback)"
fi
# LLM configuration
@@ -799,11 +866,12 @@ CADDYEOF
step_services() {
info "Step 6: Starting Docker services"
# Build GPU/CPU image from source (always needed — no prebuilt image)
local build_svc="$MODEL_MODE"
info "Building $build_svc image (first build downloads ML models, may take a while)..."
compose_cmd build "$build_svc"
ok "$build_svc image built"
# Build GPU image from source (only for --gpu mode)
if [[ "$MODEL_MODE" == "gpu" ]]; then
info "Building gpu image (first build downloads ML models, may take a while)..."
compose_cmd build gpu
ok "gpu image built"
fi
# Build or pull backend and frontend images
if [[ "$BUILD_IMAGES" == "true" ]]; then
@@ -871,25 +939,29 @@ step_services() {
step_health() {
info "Step 7: Health checks"
# Specialized model service (gpu or cpu)
local model_svc="$MODEL_MODE"
info "Waiting for $model_svc service (first start downloads ~1GB of models)..."
local model_ok=false
for i in $(seq 1 120); do
if curl -sf http://localhost:8000/docs > /dev/null 2>&1; then
model_ok=true
break
# Specialized model service (only for --gpu mode)
if [[ "$MODEL_MODE" == "gpu" ]]; then
info "Waiting for gpu service (first start downloads ~1GB of models)..."
local model_ok=false
for i in $(seq 1 120); do
if curl -sf http://localhost:8000/docs > /dev/null 2>&1; then
model_ok=true
break
fi
echo -ne "\r Waiting for gpu service... ($i/120)"
sleep 5
done
echo ""
if [[ "$model_ok" == "true" ]]; then
ok "gpu service healthy (transcription + diarization)"
else
warn "gpu service not ready yet — it will keep loading in the background"
warn "Check with: docker compose -f docker-compose.selfhosted.yml logs gpu"
fi
echo -ne "\r Waiting for $model_svc service... ($i/120)"
sleep 5
done
echo ""
if [[ "$model_ok" == "true" ]]; then
ok "$model_svc service healthy (transcription + diarization)"
else
warn "$model_svc service not ready yet — it will keep loading in the background"
warn "Check with: docker compose -f docker-compose.selfhosted.yml logs $model_svc"
elif [[ "$MODEL_MODE" == "cpu" ]]; then
ok "CPU mode — ML processing runs in-process on server/worker (no separate service)"
elif [[ "$MODEL_MODE" == "hosted" ]]; then
ok "Hosted mode — ML processing via remote GPU service (no local health check)"
fi
# Ollama (if applicable)